6 Tips for readable Stateflow charts

On the heels of a popular “5 tips for readable Simulink models” I am following up with a companion post.  While much of this material can be found in the “Stateflow best practices” document found in this sites reference section these are the 5 I find most critical.

Background

First a few background concepts. The concepts of “levels” in a Stateflow Chart is a measure of how many states exist within a state.  The count starts at the highest level state and increments for each substate.

levels

Stateflow includes the model construct of a “Subchart.”  Like subsystems in Simulink, subcharts encapsulate all the states and transitions in the state into a single “masked state.”

When counting levels a subcharted state counts as one state regardless of how many states exist within the subchart.

#1 Consistency

There are two main aspects to consistency in Stateflow charts decomposition of transition information and placement of transition information.

Transition information consists of both the transition condition (or event) and the action.

corrected

The image above shows 4 methods for decomposing the transition condition and action.  In general, I recommend a separate transition for the condition and the action.  This is for two reasons.  First, for complex conditions, the length of the text can make it difficult to read; adding in additional text in the form of the action just aggravates this issue.  Second, by placing the action on a second line it is possible for multiple transitions to use the same action.

A slight modification to the previous image canc2 show the importance of constant placement.  If the placement of information is inconsistent, e.g. in some cases above and some below, left or right, it becomes difficult to associate the transition information with a given transition.

#2 Maximum states per level & maximum levels per chart

For any given State I recommend a maximum of 3 levels of depth; if more than 3 levels are required consider creating a subchart for the part of the state requiring greater depth.

levelsLikewise, I recommend an absolute maximum depth of 5 states.  The first recommendation promotes readability at any given level within the chart.  The second recommendation promotes understanding of the chart as a whole.

#3 Number of States

As a general rule of thumb, I limit the number of states that I have on any given level to between 30 and 50.  When I find the number of states at a level exceeding that value I repartition the chart using subcharts.

#4 Resize!

Even more than in Simulink, resizing states can dramatically improve the readability of the chart.  There are 3 things to consider in the size of the state

  1. Is the state large enough to hold all the text in the state
    overflow.jpg
  2. Is the state large enough to have all of the transitions reasonably spaced out (e.g. will the text on the ouput transitions be readable?)
    bigEnough
  3. Is the state larger than it needs to be?  When states are larger than required they take up valuable screen space.
    toBig

#5 Stright lines, please

In the majority of cases the use of straight lines, with junctions to help with routing, provide the clearest diagram appearance.  The exception to this recommendation is for “self-loop back” transitions such as resets.

selfLoop

#6 Temporal logic!

Use of temporal logic, instead of self-defined counters, ensures that the duration intent is clear in the chart.timverVCnt

In this example, if the time step is equal to 0.01 seconds then the two transitions result in the same action (transitioning after 1 second).  However if the time step is something other than 0.01 seconds the evaluation would be different.  Because of this when the intention is to transition after a set time temporal logic is always preferable.

Final thoughts

Again these are just a few tips on how to make your Stateflow charts more readable.  I would, as always, be happy to hear your suggestions.

 

Writing clear requirements

This past week I have been reviewing the NASA Systems Engineering Handbook.  As I read through it I was struck by their description of the requirements writing process.  With this post, I will share a few thoughts.

Requirement inputs

In the diagram that follows they call out four inputs to the requirements writing process.  Two out of the four are frequently missed in the development of requirements. “Baseline enabling support strategies” and “Measure of effectiveness” writtingRequirements

From the NASA document:

Baselined Enabling Support Strategies: These describe the enabling products that were identified in the Stakeholder Expectations Definition Process as needed to develop, test, produce, operate, or dispose of the end product. They also include descriptions of how the end product will be supported throughout the life cycle.

What makes this unique?  By looking at the enabling products it expands the requirement writing to the system level.  E.g. the requirement is not an individual part, it can leverage existing infrastructure.

Measures of Effectiveness: These MOEs were identified during the Stakeholder Expectations Definition Process as measures that the stakeholders deemed necessary to meet in order for the project to be considered a success (i.e., to meet success criteria).

What makes this unique?  Writing test criteria is standard for requirements (or at least it should be.)  What is unique is explicitly calling out the stakeholder requirements of success in a defined and agreed upon document.  Note, the stakeholder requirements are not how the requirement document will present the requirements.

Requirement metadata

The metadata associated with requirements is often overlooked (or ignored in the  tools that support it.)  The intent behind the metadata is to

  1. Make maintenance easier
  2. Support traceability
  3. Support project planning

reqMeta

The table above provides insight in how the objectives can be met.  First, by providing ownership information it supports the maintenance objective by removing the question of whom to contact when there are questions on the requirements.  Second, traceability is explicitly called out in this table.

Finally the project planning aspect.  In explicitly specifying the verification method, lean and level better estimates of the time required for validation can be assigned.

Validation of requirements

The final aspect I want to comment on, though the whole document is worth reading, is the validation of requirements

validateRequirents

The NASA document defines 6 aspects for validating the requirements.  What is significant is that they explicitly defining validation of requirements as a step in the development process.  Multiple times in this Model-Based Design blog I have stressed the importance of using simulation to quickly detect design errors.  However, no amount of simulation can find an error if the requirements are not correctly defined.

Final thoughts

Go take a look at the NASA systems engineering document.  It is well worth the read.

5 tips for more readable Simulink models

With this post, I will share some of the methods I have used over the years to make my Simulink models more readable.

Resize subsystems and reorder ports

Resizing subsystems is a common suggestion to making diagrams more readable.  A step beyond that is to reorder the ports so that the connections between multiple subsystems are more readable.

With this Before/After example, I have done three things

  1. Resized the source and destination blocks
  2. Changed the port order on the source block
  3. Offset the two destination blocks

MATLAB functions for equations

When I am entering in an equation into Simulink, I ask myself 2 questions

  1. Is there a built-in function for this equation (e.g., Integrators, Transfer Functions, table lookups)?  Use the built-in function
  2. Is the equation more than 3 operations long?  Use a MATLAB function.

In text form, the Pythagorean theorem is quickly recognized and understood.  When written out as a Simulink equation it takes slightly longer to understand.

Note: I do have a caveat, if the mathematical operations are series of gains, for instance when converting from one unit to another, then keeping the calculations in Simulink is fine.

Use of  Virtual busses for conditionally executed subsystems

Merging data from conditionally executed subsystems requires the use of the Merge block.  When the subsystems have multiple output ports routing the data quickly becomes cumbersome.  This can be addressed by creating a virtual bus to pack and then unpack the signals.

virtBus

Note: Using a virtual bus will allow Simulink/Embedded Coder to optimize the memory placement of the signals.  If a structure is desired, then a bus object should be defined and used.

The rule of 40, 5 and 2

When I create a Simulink subsystem, I aim to have a limited number of active blocks in the subsystem (40).  A limited number of used inputs (5) and a limited number of calculated outputs (2).

  1. Active blocks:  Any block that performs an operation.  This count does not include inports, outports, mux, demux, bus…
    1. Why: When the total number of blocks goes above 40 the ability to understand what is going on in the subsystem decreases.
  2. Used inputs: Bus signals can enter the subsystem, and only one signal off of the bus may be used.  A “used signal” is one that is actively used as part of the subsystems calculations.
    1. Why: The number of used inputs is a good metric for how “focused” the subsystem is in addressing a specific issue.
  3. The number of outputs: directly relates to the first two metrics, e.g.h, how focused the subsystem is on a specific issue.

Notes: Subsystems that are integration subsystems (see this Model Architecture post) can and should break this rule.)

Stay out of the deep end, beware of breadth

As a general rule of thumb, I recommend that models have a “depth” of 3.  Navigation up and down the hierarchy quickly can lose a reviewer.  Likewise, for given model, I recommend between 30 and 60 subsystems in total.

Image result for pool deep end

This recommendation holds for a single model.  For integration models, each “child” model should be treated as a single unit.

Final thoughts

These are just a few of the recommendations that I have hit upon in the past 18 years.  I would be curious to hear your thoughts and recommendations.

Interfacing with hardware in Model-Based Design context

Interfaces between low-level device drivers and algorithmic software have multiple unique issues.  These issues exist in traditional text-based development processes and in MBD workflows.  Let’s review the challenges and methods for meeting the challenges.

Hardware challenge

Image result for hardware challengeI decompose the hardware challenges into two categories; conceptual and technical.

Conceptual challenges

For software engineers, the concepts behind hardware interfaces are frequently a source of error.

  1. Finite resolution:  Physical hardware has a finite resolution.  A 12-bit A/D converter will not provide data at a higher resolution than 12-bits.
  2. Temporal non-determinism:  Readings from hardware, unless specifically configured to do so, are not assured to be from the same iteration of the algorithm.
  3. Corrupted data: Data from hardware sources can be corrupted in multiple methods.  The software needs to handle these corruptions in a robust fashion.

Technical challenges

The technical challenges are standard component-to-component interface issues.

  1. Data conversion: Information comes from the hardware in units of counts or encoded information.  This data needs to be converted into engineering units for use in the system.
  2. Hardware/Software interface architecture:  The method for interfacing the hardware and software components requires a stricter encapsulation than software-to-software architectural components.
  3. Component triggering: Hardware components can be triggered using one of three basic methods.  Schedule based triggering, event-based triggers or interrupt based triggers.

Addressing the hardware challenges

Understanding the hardware challenges we can now address them.  The conceptual challenges are addressed through education.

Conceptual challenges

  1. Finite resolution: Analog-to-Digital Converter Testing
    Kent H. Lundberg (MIT)
  2. Temporal non-determinism: The temporal logic of programs
  3. Corrupted data: Removing spikes from signals

Image result for hardware addresses

Technical challenges

Technical challenges are handled with education and patterns.

  • Data conversion: Data conversion is done through any number of simple algorithms, from y = m*x + b equations, table look ups or polynomials.
    scaling
  • Hardware/Software interface architecture:  Interfaces to the hardware run through a Hardware Abstraction Layer (HAL).  The HAL functions can be directly called from within the Model-Based Design environment.
    Related image
    Because the HAL is a discreet function the call to the hardware should encapsulated on a per function basis.  (Note: multiple calls can be made to the function if it is reentrant, however this tends to be less efficient)
    hardwareScaling
    The connection and scaling of the hardware is broken into 3 sub-components shown above.

    • Access to the low level device drivers
    • Data filtering
    • Data scaling
      The top level model architecture interfaces the
  • Component triggering: Hardware components can be triggered using one of three basic methods.  Schedule based triggering, event-based triggers or interrupt based triggers.  Information on how to trigger component can be found here.

Final thoughts

Well defined interfaces between hardware and software is provide clarity in communicating design intent.  The model architecture can be developed from the basic architecture proposed here, with the hardware inputs and outputs being a top level integration system.

 

 

Image result for marriage of hardware and software